Untrained DNN for Channel Estimation of RIS-Assisted Multi-User OFDM System with Hardware Impairments

TL;DR

This paper introduces an untrained deep neural network approach based on the deep image prior for accurate, low-complexity channel estimation in RIS-assisted multi-user OFDM systems, robust to hardware impairments.

Contribution

It presents a novel untrained DNN method using deep image prior for denoising and improving channel estimation in RIS-assisted systems with hardware impairments.

Findings

High accuracy in channel estimation compared to conventional methods

Low computational complexity of the proposed approach

Robustness to hardware impairments and interference

Abstract

Reconfigurable intelligent surface (RIS) is an emerging technology for improving performance in fifth-generation (5G) and beyond networks. Practically channel estimation of RIS-assisted systems is challenging due to the passive nature of the RIS. The purpose of this paper is to introduce a deep learning-based, low complexity channel estimator for the RIS-assisted multi-user single-input-multiple-output (SIMO) orthogonal frequency division multiplexing (OFDM) system with hardware impairments. We propose an untrained deep neural network (DNN) based on the deep image prior (DIP) network to denoise the effective channel of the system obtained from the conventional pilot-based least-square (LS) estimation and acquire a more accurate estimation. We have shown that our proposed method has high performance in terms of accuracy and low complexity compared to conventional methods. Further, we have shown that the proposed estimator is robust to interference caused by the hardware impairments at the transceiver and RIS.